OpenRaiser/Intern-Atlas huggingface dataset:
2-DOF block pole placement control application to: have-dash-II missile
Venue: arXiv cs.SY 2016
Year: 2016

In a multivariable servomechanism design, it is required that the output vector tracks a certain reference vector while satisfying some desired transient specifications, for this purpose a 2DOF control law consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed using block pole placement technique by assigning a set of desired Block poles in different canonical forms. The resulting control is simulated for linearized model of the HAVE DASH II BTT missile, numerical results are analyzed and compared in terms of transient response, gain magnitude, performance robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
Service Intelligence Oriented Distributed Data Stream Integration
Venue: arXiv cs.SE 2016
Year: 2016

Software as a service (SaaS) has recently enjoyed much attention as it makes the use of software more convenient and cost-effective. At the same time, the arising of users' expectation for high quality service such as real-time information or functionality provisioning brings about new challenges: to satisfy such (near) real-time requirements, real-time monitoring and effective processing of streaming data is necessary. However, due to the composition structure and multi-instance property of services, service data streams are often distributed, hard to synchronize and aggregate. We tackles these challenges by (1) proposing systematic associate strategies for relating distributed data; (2) introducing a new small window array mechanism for aggregating distributed data; (3) setting window parameters based on the cumulative distribution function (CDF) method; and (4) modeling streaming operators with queuing models for performance evaluation and prediction. Experiments show that our approach has good accuracy, completeness and acceptable performance measurement in processing distributed service data streams.
Fast Object Localization Using a CNN Feature Map Based Multi-Scale Search
Venue: arXiv cs.CV 2016
Year: 2016

Object localization is an important task in computer vision but requires a large amount of computational power due mainly to an exhaustive multiscale search on the input image. In this paper, we describe a near real-time multiscale search on a deep CNN feature map that does not use region proposals. The proposed approach effectively exploits local semantic information preserved in the feature map of the outermost convolutional layer. A multi-scale search is performed on the feature map by processing all the sub-regions of different sizes using separate expert units of fully connected layers. Each expert unit receives as input local semantic features only from the corresponding sub-regions of a specific geometric shape. Therefore, it contains more nearly optimal parameters tailored to the corresponding shape. This multi-scale and multi-aspect ratio scanning strategy can effectively localize a potential object of an arbitrary size. The proposed approach is fast and able to localize objects of interest with a frame rate of 4 fps while providing improved detection performance over the state-of-the art on the PASCAL VOC 12 and MSCOCO data sets.
Spatiotemporal Articulated Models for Dynamic SLAM
Venue: arXiv cs.RO 2016
Year: 2016

We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future mo- tions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incorporating it within a standard simultaneous localization and mapping (SLAM) pipeline for mapping and robot localization in previously unexplored dynamic environments. Our method is able to localize the robot and map a dynamic scene by explaining the observed motion in the world. We demonstrate the effectiveness of the proposed framework for both simulated and real-world dynamic environments.
Cooperative Network Node Positioning Techniques Using Underwater Radio Communications
Venue: arXiv cs.NI 2016
Year: 2016

We analyze the problem of localization algorithms for underwater sensor networks. We first characterize the underwater channel for radio communications and adjust a linear model with measurements of real transmissions. We propose an algorithm where the sensor nodes collaboratively estimate their unknown positions in the network. In this setting, we assume low connectivity of the nodes, low data rates, and nonzero probability of lost packets in the transmission. Finally, we consider the problem of a node estimating it's position in underwater navigation. We also provide simulations illustrating the previous proposals.
Binary Polar Codes are Optimized Codes for Bitwise Multistage Decoding
Venue: arXiv cs.IT 2016
Year: 2016

Polar codes are considered the latest major breakthrough in coding theory. Polar codes were introduced by Arıkan in 2008. In this letter, we show that the binary polar codes are the same as the optimized codes for bitwise multistage decoding (OCBM), which have been discovered before by Stolte in 2002. The equivalence between the techniques used for the constructions and decodings of both codes is established.
Dissecting a Social Botnet: Growth, Content and Influence in Twitter
Venue: arXiv cs.CY 2016
Year: 2016

Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users.
Online Multi-Target Tracking Using Recurrent Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.
A Practical Approach to Spatiotemporal Data Compression
Venue: arXiv cs.MM 2016
Year: 2016

Datasets representing the world around us are becoming ever more unwieldy as data volumes grow. This is largely due to increased measurement and modelling resolution, but the problem is often exacerbated when data are stored at spuriously high precisions. In an effort to facilitate analysis of these datasets, computationally intensive calculations are increasingly being performed on specialised remote servers before the reduced data are transferred to the consumer. Due to bandwidth limitations, this often means data are displayed as simple 2D data visualisations, such as scatter plots or images. We present here a novel way to efficiently encode and transmit 4D data fields on-demand so that they can be locally visualised and interrogated. This nascent "4D video" format allows us to more flexibly move the boundary between data server and consumer client. However, it has applications beyond purely scientific visualisation, in the transmission of data to virtual and augmented reality.
Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a basic cellular network model. Then, it characterizes signal-to-interference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the baseline unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. Finally, we point out future research directions.
DENSER Cities: A System for Dense Efficient Reconstructions of Cities
Venue: arXiv cs.CV 2016
Year: 2016

This paper is about the efficient generation of dense, colored models of city-scale environments from range data and in particular, stereo cameras. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost. The computational and memory requirements of large dense models can be prohibitive. We provide the theory and the system needed to create city-scale dense reconstructions. To do so, we apply a regularizer over a compressed 3D data structure while dealing with the complex boundary conditions this induces during the data-fusion stage. We show that only with these considerations can we swiftly create neat, large, "well behaved" reconstructions. We evaluate our system using the KITTI dataset and provide statistics for the metric errors in all surfaces created compared to those measured with 3D laser. Our regularizer reduces the median error by 40% in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For subjective analysis, we provide a qualitative review of 6.1 km of our dense reconstructions in an attached video. These are the largest dense reconstructions from a single passive camera we are aware of in the literature.
The Existence of the Tau One-Way Functions Class as a Proof that P != NP
Venue: arXiv cs.CC 2016
Year: 2016

We prove that P != NP by proving the existence of a class of functions we call Tau, each of whose members satisfies the conditions of one-way functions. Each member of Tau is a function computable in polynomial time, with negligible probability of finding its inverse by any polynomial probabilistic algorithm. We also prove that no polynomial-time algorithm exists to compute the inverse of members of Tau, and that the problem of computing the inverse of Tau cannot be reduced to FSAT in polynomial time.
HordeQBF: A Modular and Massively Parallel QBF Solver
Venue: arXiv cs.LO 2016
Year: 2016

The recently developed massively parallel satisfiability (SAT) solver HordeSAT was designed in a modular way to allow the integration of any sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a massively parallel QBF solver---HordeQBF. In this paper we describe the details of this integration and report on results of the experimental evaluation of HordeQBF's performance. HordeQBF achieves superlinear average and median speedup on the hard application instances of the 2014 QBF Gallery.
Extending Homotopy Type Theory with Strict Equality
Venue: arXiv cs.LO 2016
Year: 2016

In homotopy type theory (HoTT), all constructions are necessarily stable under homotopy equivalence. This has shortcomings: for example, it is believed that it is impossible to define a type of semi-simplicial types. More generally, it is difficult and often impossible to handle towers of coherences. To address this, we propose a 2-level theory which features both strict and weak equality. This can essentially be represented as two type theories: an "outer" one, containing a strict equality type former, and an "inner" one, which is some version of HoTT. Our type theory is inspired by Voevosky's suggestion of a homotopy type system (HTS) which currently refers to a range of ideas. A core insight of our proposal is that we no not need any form of equality reflection in order to achieve what HTS was suggested for. Instead, having unique identity proofs in the outer type theory is sufficient, and it also has the meta-theoretical advantage of not breaking decidability of type checking. The inner theory can be an easily justifiable extensions of HoTT, allowing the construction of "infinite structures" which are considered impossible in plain HoTT. Alternatively, we can set the inner theory to be exactly the current standard formulation of HoTT, in which case our system can be thought of as a type-theoretic framework for working with "schematic" definitions in HoTT. As demonstrations, we define semi-simplicial types and formalise constructions of Reedy fibrant diagrams.
Employee Trust Based Industrial Device Deployment and Initial Key Establishment
Venue: arXiv cs.CR 2016
Year: 2016

An efficient key management system is required to support cryptography. Most key management systems use either pre-installed shared keys or install initial security parameters using out-of-band channels. These methods create an additional burden for engineers who manage the devices in industrial plants. Hence, device deployment in industrial plants becomes a challenging task in order to achieve security. In this work, we present a device deployment framework that can support key management using the existing trust towards employees in a plant. This approach reduces the access to initial security parameters by employees, rather it helps to bind the trust of the employee with device commissioning. Thus, this approach presents a unique solution to the device deployment problem. Further, through a proof-of-concept implementation and security analysis using the AVISPA tool, we present that our framework is feasible to implement and satisfies our security objectives.
Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly
Venue: arXiv cs.CV 2016
Year: 2016

Today's person detection methods work best when people are in common upright poses and appear reasonably well spaced out in the image. However, in many real images, that's not what people do. People often appear quite close to each other, e.g., with limbs linked or heads touching, and their poses are often not pedestrian-like. We propose an approach to detangle people in multi-person images. We formulate the task as a region assembly problem. Starting from a large set of overlapping regions from body part semantic segmentation and generic object proposals, our optimization approach reassembles those pieces together into multiple person instances. It enforces that the composed body part regions of each person instance obey constraints on relative sizes, mutual spatial relationships, foreground coverage, and exclusive label assignments when overlapping. Since optimal region assembly is a challenging combinatorial problem, we present a Lagrangian relaxation method to accelerate the lower bound estimation, thereby enabling a fast branch and bound solution for the global optimum. As output, our method produces a pixel-level map indicating both 1) the body part labels (arm, leg, torso, and head), and 2) which parts belong to which individual person. Our results on three challenging datasets show our method is robust to clutter, occlusion, and complex poses. It outperforms a variety of competing methods, including existing detector CRF methods and region CNN approaches. In addition, we demonstrate its impact on a proxemics recognition task, which demands a precise representation of "whose body part is where" in crowded images.
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections. We do this by observing that the guarantees of algorithms with bounded approximate max-information are sufficient to correct the $p$-values of adaptively chosen hypotheses, and then by proving that algorithms that satisfy $(ε,δ)$-differential privacy have bounded approximate max information when their inputs are drawn from a product distribution. This substantially extends the known connection between differential privacy and max-information, which previously was only known to hold for (pure) $(ε,0)$-differential privacy. It also extends our understanding of max-information as a partially unifying measure controlling the generalization properties of adaptive data analyses. We also show a lower bound, proving that (despite the strong composition properties of max-information), when data is drawn from a product distribution, $(ε,δ)$-differentially private algorithms can come first in a composition with other algorithms satisfying max-information bounds, but not necessarily second if the composition is required to itself satisfy a nontrivial max-information bound. This, in particular, implies that the connection between $(ε,δ)$-differential privacy and max-information holds only for inputs drawn from product distributions, unlike the connection between $(ε,0)$-differential privacy and max-information.
Understanding How Image Quality Affects Deep Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting
Venue: arXiv cs.IR 2016
Year: 2016

Supervised term weighting could improve the performance of text categorization. A way proven to be effective is to give more weight to terms with more imbalanced distributions across categories. This paper shows that supervised term weighting should not just assign large weights to imbalanced terms, but should also control the trade-off between over-weighting and under-weighting. Over-weighting, a new concept proposed in this paper, is caused by the improper handling of singular terms and too large ratios between term weights. To prevent over-weighting, we present three regularization techniques: add-one smoothing, sublinear scaling and bias term. Add-one smoothing is used to handle singular terms. Sublinear scaling and bias term shrink the ratios between term weights. However, if sublinear functions scale down term weights too much, or the bias term is too large, under-weighting would occur and harm the performance. It is therefore critical to balance between over-weighting and under-weighting. Inspired by this insight, we also propose a new supervised term weighting scheme, regularized entropy (re). Our re employs entropy to measure term distribution, and introduces the bias term to control over-weighting and under-weighting. Empirical evaluations on topical and sentiment classification datasets indicate that sublinear scaling and bias term greatly influence the performance of supervised term weighting, and our re enjoys the best results in comparison with existing schemes.
Ergodic Theory for Controlled Markov Chains with Stationary Inputs
Venue: arXiv cs.PF 2016
Year: 2016

Consider a stochastic process $\{X(t)\}$ on a finite state space $ {\sf X}=\{1,\dots, d\}$. It is conditionally Markov, given a real-valued `input process' $\{ζ(t)\}$. This is assumed to be small, which is modeled through the scaling, \[ ζ_t = \varepsilon ζ^1_t, \qquad 0\le \varepsilon \le 1\,, \] where $\{ζ^1(t)\}$ is a bounded stationary process. The following conclusions are obtained, subject to smoothness assumptions on the controlled transition matrix and a mixing condition on $\{ζ(t)\}$: (i) A stationary version of the process is constructed, that is coupled with a stationary version of the Markov chain $\{X^\bullet$(t)\}obtained with $\{ζ(t)\}\equiv 0$. The triple $(\{X(t)\}, \{X^\bullet(t)\},\{ζ(t)\})$ is a jointly stationary process satisfying \[ {\sf P}\{X(t) \neq X^\bullet(t)\} = O(\varepsilon) \] Moreover, a second-order Taylor-series approximation is obtained: \[ {\sf P}\{X(t) =i \} ={\sf P}\{X^\bullet(t) =i \} + \varepsilon^2 \varrho(i) + o(\varepsilon^2),\quad 1\le i\le d, \] with an explicit formula for the vector $\varrho\in\mathbb{R}^d$. (ii) For any $m\ge 1$ and any function $f\colon \{1,\dots,d\}\times \mathbb{R}\to\mathbb{R}^m$, the stationary stochastic process $Y(t) = f(X(t),ζ(t))$ has a power spectral density $\text{S}_f$ that admits a second order Taylor series expansion: A function $\text{S}^{(2)}_f\colon [-π,π] \to \mathbb{C}^{ m\times m}$ is constructed such that \[ \text{S}_f(θ) = \text{S}^\bullet_f(θ) + \varepsilon^2 \text{S}_f^{(2)}(θ) + o(\varepsilon^2),\quad θ\in [-π,π] . \] An explicit formula for the function $\text{S}_f^{(2)}$ is obtained, based in part on the bounds in (i). The results are illustrated using a version of the timing channel of Anantharam and Verdu.
Towards Better Models of Externalities in Sponsored Search Auctions
Venue: arXiv cs.GT 2016
Year: 2016

Sponsored Search Auctions (SSAs) arguably represent the problem at the intersection of computer science and economics with the deepest applications in real life. Within the realm of SSAs, the study of the effects that showing one ad has on the other ads, a.k.a. externalities in economics, is of utmost importance and has so far attracted the attention of much research. However, even the basic question of modeling the problem has so far escaped a definitive answer. The popular cascade model is arguably too idealized to really describe the phenomenon yet it allows a good comprehension of the problem. Other models, instead, describe the setting more adequately but are too complex to permit a satisfactory theoretical analysis. In this work, we attempt to get the best of both approaches: firstly, we define a number of general mathematical formulations for the problem in the attempt to have a rich description of externalities in SSAs and, secondly, prove a host of results drawing a nearly complete picture about the computational complexity of the problem. We complement these approximability results with some considerations about mechanism design in our context.